GenAI Machine Learning Engineer, Performance Optimization

P-984

Founded in late 2020 by a small group of machine learning engineers and researchers, MosaicML enables companies to securely fine-tune, train and deploy custom AI models on their own data, for maximum security and control. Compatible with all major cloud providers, the MosaicML platform provides maximum flexibility for AI development. Introduced in 2023, MosaicML’s pretrained transformer models have established a new standard for open source, commercially usable LLMs and have been downloaded over 3 million times. MosaicML is committed to the belief that a company’s AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all.

Now part of Databricks since July 2023, we are passionate about enabling our customers to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI platform so our customers can use deep data insights to improve their business. We leap at every opportunity to solve technical challenges, striving to empower our customers with the best data and AI capabilities.

You will:

• Explore and analyze performance bottlenecks in ML training and inference
• Design, implement and benchmark libraries and methods to overcome aforementioned bottlenecks
• Build tools for performance profiling, analysis, and estimation for ML training and inference
• Balance the tradeoff between performance and usability for our customers
• Facilitate our community through documentation, talks, tutorials, and collaborations
• Collaborate with external researchers and leading AI companies on various efficiency methods

We look for:

• Hands on experience the internals of deep learning frameworks (e.g. PyTorch, TensorFlow) and deep learning models
• Experience with high-performance linear algebra libraries such as cuDNN, CUTLASS, Eigen, MKL, etc.
• General experience with the training and deployment of ML models
• Experience with compiler technologies relevant to machine learning
• Experience with distributed systems development or distributed ML workloads
• Hands on experience with writing CUDA code and knowledge of GPU internals (Preferred)
• Publications in top tier ML or System Conferences such as MLSys, ICML, ICLR, KDD, NeurIPS (Preferred)

We value candidates who are curious about all parts of the company's success and are willing to learn new technologies along the way.